Google Scholar Citations

Professor Enke received his PhD in Engineering Management in 1997, his MS in Engineering Management in 1994, and his BS in Electrical Engineering in 1990, all from UMR. Prior to returning to Missouri S&T as Professor and Chair of the EMSE department in the spring of 2012, Professor Enke was the H. Michael and Laurie Krimbill Faculty Fellow of Finance and Chair of the Department of Finance & Operations Management at The University of Tulsa. He was previously on the faculty of Missouri S&T from 2000 to 2007 as an Assistant/Associate Professor within the EMSE department. He joined the faculty of Binghamton University in 1999 and was on the faculty at the University of Michigan – Dearborn during 1998. He was employed for six years by the McDonnell Douglas Corporation (now The Boeing Company) prior to his graduate studies. Professor Enke is currently the Interim Associate Dean for the Kummer College, as well as a Curators’ Distinguished Teaching Professor in the department of Engineering Management and Systems Engineering. 

Professor Enke is the founder of the Laboratory for Investment and Financial Engineering (LIFE) at Missouri S&T and is a member of the Missouri S&T Intelligent Systems Center. He is an active reviewer for multiple finance and computational intelligence journals, is an area editor for the journal The Engineering Economist (topics: AI, machine learning, computational intelligence, data analytics) and was a past Co-Chair of the Artificial Neural Networks in Engineering conference. Professor Enke (Google Scholar, Scopus) has published over 130 journal publications, book chapters, and conference proceedings, and has been a part of research teams that have secured external funding from industry and government agencies. He has been the recipient of 8 research paper awards and 12 outstanding teaching awards.

Dr. Robert Marley - Bio

Dr. Robert Marley - Brief CV

Dr. Robert Marley - Full CV

Academic Positions:

- Curators' Professor, Department of Mechanical and Aerospace Engineering, Missouri S&T (2008-present)
- Professor, Department of Mechanical and Aerospace Engineering, Missouri S&T(1997-2007)
- Associate Professor, Department of Mechanical and Aerospace Engineering and Engineering Mechanics, Missouri S & T (1991-1997)
- Assistant Professor, Department of Mechanical and Aerospace Engineering and Engineering Mechanics, Missouri S & T (1985-1991)
- Graduate Research Assistant, Virginia Polytechnic Institute and State University, Blacksburg, VA (1982-85)

Non-Academic Positions:

- Assistant Civil Engineer, Structural Design Group, Tata Consulting Engineers, India (1980-82)

https://mo-cci.mst.edu/

Jagannathan Sarangapani is at the Missouri University of Science and Technology, Rolla, MO, USA, where he is a Rutledge-Emerson Distinguished Professor and was Site Director for the graduated NSF Industry/University Cooperative Research Center on Intelligent Maintenance Systems. He also has a courtesy appointment with the Department of Computer Science. He has co-authored 179 peer-reviewed journal articles, over 289 refereed IEEE conference articles, several book chapters, and co-authored four books and two edited books. He holds 21 patents, one defense publication, with several pending. He has supervised the completion of over 30 doctoral students and 31 M.S. thesis students. His research funding is in excess of $18.6 million dollars (his shared credit $10.57 million) from NSF, NASA, AFOSR, ARO, ONR, AFRL, Boeing, Honeywell, Sandia and from other companies. His current research interests include neural network learning, adaptation, decision making and control, networked control systems/cyber physical systems, prognostics/bigdata, and autonomous systems/robotics with healthcare applications.  He served/serving on various editorial boards and as a co-editor for the IET Book series on Control.

Journal Papers (recent)

1. Tejalal Choudhary, Vipul Kumar Mishra, Anurag Goswami, Jagannathan Sarangapani, “A transfer learning with structured filter pruning approach for improved breast cancer classification on point-of-care devices”, Journal of Computers in Biology and Medicine, accepted for publication, April 2021.
2. Raghavan, S. Jagannathan, and V. Samaranayake, "A game-theoretic approach for addressing domain-shift in big-data", IEEE Transactions on Bigdata, accepted for publication, April 2021.
3. Moghadam, P. Natarajan, and S. Jagannathan, “Online optimal adaptive control of partially uncertain nonlinear discrete-time systems using multilayer neural networks”, IEEE Transactions on Neural Networks and Learning Systems, accepted for publication, February 2021.
4. Jinna Li, Xiao, T. Chai, F.L. Lewis, and S. Jagannathan, " Adaptive interleaved reinforcement learning: robust stability of affine nonlinear systems with unknown uncertainty", IEEE Transactions on Neural Networks and Learning Systems, accepted for publication, October 2020.
5. Prakash, L. Behera, S. Mohan, and S. Jagannathan, “Dual loop optimal control of a robot manipulator and its application in warehouse automation”, IEEE Transactions on Automation Science and Engineering, accepted for publication, September 2020.
6. Raghavan, Shweta Garg, S. Jagannathan, and V. Samaranayake, "Distributed min-max learning scheme for neural network with applications to high dimensional classification", IEEE Transactions on Neural Networks and Learning Systems, accepted for publication, August 2020.
7. Narayanan, H. Moderes, S. Jagannathan and F. L. Lewis, “Event-driven off-policy reinforcement learning for control of interconnected systems”, IEEE Transactions on Cybernetics, accepted for publication, April 2020.
8. Haifeng Niu and S. Jagannathan, “Flow based attack detection and accommodation for networked control systems”, International Journal of Control, vol. 94, no. 3, 834-847, March 2021.
9. Natarajan, R. Moghadam, and S. Jagannathan, “Online deep neural network-based feedback control of a Lutein bioprocess”, Journal of Process Control, vol. 98, pp. 41-51, 2021.
10. Raghavan, S. Jagannathan, V. Samaranayake, “Direct error-driven learning for deep neural networks with applications to big-data”, IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 5, pp. 1763-1770, May 2020.

 

Conference Papers (representative)

1. Rohollah Moghadam, and S. Jagannathan, “Optimal adaptive regulation of uncertain linear continuous-time systems with state and input delays”, of the IEEE Conference on Decision and Control, pp. 132-137, December 2020.
2. Rohollah Moghadam, P. Rajan, and S. Jagannathan, “Multilayer neural network-based optimal adaptive tracking control of partially uncertain nonlinear discrete-time systems”, of the IEEE Conference on Decision and Control, pp. 2204-2209, December 2020.
3. Jinna Li, Zhenfei Xiao, TianYou Chai, Frank L. Lewis, and S. Jagannathan, “Off-policy Q-learning for anti-interference control of multi-player systems”, Proc of the IFAC World Congress, Berlin Germany, July 2020.
4. Rohollah Moghadam, Pappa Natarajan, Krishnan Raghavan and S. Jagannathan, “Online optimal adaptive control of a class of uncertain nonlinear discrete-time systems”, of the IEEE International Joint Conference on Neural Networks (IJCNN) as part of WCCI, pp. 1-6, August 2020.
5. Moghadam and S. Jagannathan, “Optimal control of linear continuous-time systems in the presence of state and input delays with application to a chemical reactor”, Proc. of American Controls Conference, pp. 999-1004, July 2020.
6. Moghadam and S. Jagannathan, “Approximate optimal adaptive control of partially unknown linear continuous-time systems with state delay”, Proc. of the IEEE Conference on Decision and Control, pp. 1985-1990, December 2019.

 

Books (edited) Published

• K. Vamvoudakis and S. Jagannathan, “Control of Complex Systems: Recent Advances and Future Directions”, Wiley, (Edited) 2016.

Book Chapter(s)

• Rohollah Moghadam, V, Narayanan, S. Jagannathan, and Krishnan Raghavan, “Optimal adaptive control of uncertain linear systems with time-delay”, Springer, in Handbook of Reinforcement Learning and Control, Editors: K.G. Vomvoudakis, Y. Wan, F. Lewis and D.Canseer, 2021.
• Krishnan Raghavan, S. Jagannathan, and V. Samaranayake, “Direct error driven learning for classification with applications to Bigdata”, Editors: W. Pedrycz and S. Chen, Deep Learning Architectures, Springer Nature, pp. 1-30, 2020.
• Hao Xu and S. Jagannathan, “Joint scheduling and event triggered optimal control design for cyber physical systems”, Editors:  Sandip Roy and Sajal Das, Principles of CPS: An Interdisciplinary Approach, Cambridge University Press, pp. 104-126, 2020.

Patents

• Al Salour, D. Trimble, J. Sarangapani, and E. Taqieddin, "Ultra-lightweight Mutual Authentication Protocol with Substitution Operation”, US Patent No. 10198605, February 5, 2019. (jointly with Boeing, St Louis)

Recent Grants (active)

• Deep Neural Network Control, PI, ONR, 2021-2025.
• Planning Grant: Engineering Research Center for Integrative Manufacturing and Remanufacturing Technologies (iMart) to Spur Rural Development, Co-PI, NSF, 2019-2021.
• A Doctoral Program in Big Data, Machine Learning, and Analytics for Security and Safety”, Co-PI, Dept. of Education, 2018-2021.
• RFID based Asset Tracking and Evolvable DNA, Honeywell, PI, 2020-2021.

Selected Awards

• 2021 University of Missouri Presidential Award for Sustained Excellence-STEM
• 2020 Best Associate Editor Award, IEEE Systems, Man, and Cybernetics-Systems.
• 2018 IEEE Control System Society’s Transition to Practice Award
• 2018 Fellow, National Academy of Inventors
• 2016 Fellow of the IEEE
• 2015 Fellow of the IET (UK)
• 2014 Fellow of the Inst. Of Measurement & Control (UK)
• 2005 Teaching Commendation Award
• Commended for Teaching Excellence in 2006-2007, 2012-2013, 2013-2014
• Outstanding Teaching Award 2014-2015, 2015-2016, 2017-2018
• Faculty Excellence Award 2005-2006, 2006-2007
• 2007 Boeing Pride Achievement Award
• 2001 University of Texas Presidential Award for Excellence (early career)
• 2001 Caterpillar Research Excellence Award
• 2000 NSF Career Award

Students Graduated (recent)

• Rohollah Moghadam, “Optimal adaptive control of timed-delay dynamical systems with known and uncertain dynamics”, October 2020. (Assistant Professor, Arkansas Tech. University)

Donald Wunsch is the Founding Director of Kummer Institute Center for Artificial Intelligence and Autonomous Systems at Missouri University of Science & Technology (Missouri S&T).    Earlier employers were: Texas Tech University, Boeing, Rockwell International, and International Laser Systems.  His education includes: Executive MBA - Washington University in St. Louis,  Ph.D., Electrical Engineering - University of Washington (Seattle), M.S.,  Applied Mathematics   (same institution),   B.S., Applied Mathematics - University of New Mexico, and Jesuit Core Honors Program, Seattle University.  Key research contributions are: Clustering; Adaptive Resonance and Reinforcement Learning architectures, hardware and applications; Neurofuzzy regression; Traveling Salesman Problem heuristics; Robotic Swarms; and Bioinformatics.   He is an IEEE Fellow and previous INNS President, INNS Fellow and Senior Fellow 2007-2013, NSF CAREER Award winner, and winner of the 2015 INNS Gabor Award.  He served as IJCNN General Chair, and on several Boards, including the St. Patrick’s School Board, IEEE Neural Networks Council, International Neural Networks Society, and the University of Missouri Bioinformatics Consortium, Chaired the Missouri S&T Information Technology and Computing Committee as well as the Student Design and Experiential Learning Center Board. 

Journal Articles

•  Lei Meng, Ah-Hwee Tan, and Donald Wunsch, "Adaptive Scaling of Cluster Boundaries for Large-scale Social Media Data Clustering," IEEE Trans. on Neural Networks and Learning Systems,  DOI: 10.1109/TNNLS.2015.2498625.
• Dao Lam, Mingzhen Wei, Donald Wunsch, "Clustering Data of Mixed Categorical and Numerical Type with Unsupervised Feature Learning," IEEE Access, Vol. 3, pp/ 1605-1613, Sept. 2015.  
• Xiuzhen Huang, Steven F. Jennings, Barry Bruce, Alison Buchan, Liming Cai, Pengyin Chen, Carole Cramer, Weihua Guan, Uwe KK Hilgert, Hongmei Jiang, Zenglu Li, Gail McClure, Donald F. McMullen, Bindu Nanduri, Andy Perkins, Bhanu Rekepalli, Saeed Salem, Jennifer Specker, Karl Walker, Donald Wunsch, Donghai Xiong, Shuzhong Zhang, Yu Zhang,  Zhongming Zhao and Jason H Moore, “Big data – a 21^st century science Maginot Line? No-boundary thinking: shifting from the big data paradigm,” Journal of BMC BioData Mining, Vol. 8, No. 7, February 2015.   
• Steven Damelin, Y. Gu, Donald Wunsch, and Rui Xu, "Fuzzy Adaptive Resonance Theory, Diffusion Maps, and their applications to Clustering and Biclustering," Mathematical Modelling of Natural Phenomena: Special Issue on Model Reduction Across Disciplines in Honor of Alexander N. Gorban, Vol. 10, No. 3, 2015, pp. 206-211.
• Gennady Fridman, Jeremy Levesley, Ivan Tyukin, and Donald Wunsch (Eds.), “Preface,” Mathematical Modelling of Natural Phenomena: Special Issue on Model Reduction Across Disciplines in Honor of Alexander N. Gorban, Vol. 10, No. 3, 2015, pp. 1-5.
• Xingang Fu, Shuhui Li, Michael Fairbank, Donald Wunsch, and Eduardo Alonso, “Training Recurrent Neural Networks with the Levenberg­Marquardt Algorithm for Optimal Control of a Grid-Connected Converter,” IEEE Transactions on Neural Networks and Learning Systems, Vol 26, No. 9, September 2015.  *

Conference Articles

• Tayo Obafemi-Ajayi, Dao Lam, T. Nicole Takahashi, Stephen Kanne, Donald Wunsch,  “Sorting the Phenotypic Heterogeneity of Autism Spectrum Disorders: a Hierarchical Clustering Model,” IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, August 12-15, 2015, Niagara Falls, Canada.
• Clayton Smith and Donald Wunsch, “Time series prediction via two-step clustering,” Proc. IEEE / INNS International Joint Conference on Neural Networks, July 12-16, 2015.
• Clayton Smith and Donald Wunsch, “Particle swarm optimization in an adaptive resonance framework,” Proc. IEEE / INNS International Joint Conference on Neural Networks, July 12-16, 2015.
• Leonardo Enzo Brito da Silva and Donald Wunsch, “Multi-Prototype Local Density-based Hierarchical Clustering,” Proc. IEEE / INNS International Joint Conference on Neural Networks, July 12-16, 2015.

Patents

• R. Dua, S.E. Watkins, D.C. Wunsch, “Neural network demodulator for optical sensor,” U.S. Patent 7,603004, Filed June 12, 2007, Issued October 13, 2009. 
• R. Meuth, J.L. Vian, E.W. Saad, D.C. Wunsch, “Adaptive multi-vehicle coverage optimization system and method, U.S. Patent 8,260,510, Filed July 12, 2012, Issued December 31, 2013. 
• R. Meuth, J.L. Vian, E.W. Saad, D.C. Wunsch, “Adaptive multi-vehicle coverage optimization system and method, U.S. Patent 8,260,485, Filed September 18, 2007, Issued September 4, 2012. 
• E.W. Saad, J.L. Vian, R.J. Meuth, and D.C. Wunsch, “Hierarchical mission management,” U.S. Patent Application 20110082717, October 5, 2009.
• E.W. Saad, S.R. Bieniawski, P.E.R. Pigg, J.L. Vian, P.M. Robinette, D.C. Wunsch, “Real time mission planning,” U.S. Patent 9064222, Applied for May 14, 2010, issued June 23, 2015.
• R. Xu, D.C. Wunsch, S. Kim, “Methods and systems for biclustering algorithm,” U.S. Patent 9043326 Filed January 28, 2012, claiming priority to Provisional U.S. Patent Application, January 28, 2011, issued May 26, 2015 

 Selected Awards

• IEEE Fellow
• INNS Fellow
• INNS Senior Fellow
• NSF CAREER Award
• INNS Gabor Award for Excellence in Neural Networks Engineering Contributions
• INNS President
• Haliburton Award for Excellence in Teaching and Research
• Charles Hedlund Distinguished Visiting Professor, American University Cairo
• IEEE Electron Devices Society Distinguished Lecturer
• IEEE Computational Intelligence Society Distinguished Lecturer
• Coauthor, IEEE Conference on Evolutionary Computation Best Overall Paper
• Master, DeTao Masters’ Academy, Shanghai
• Eagle Scout